深度学习工具包

Love The Way You Lie 2022-11-22 05:13 34阅读 0赞

# Software links #

[Theano][] – CPU/GPU symbolic expression compiler in python (from LISA lab at University of Montreal)

[Pylearn2][] \- Pylearn2 is a library designed to make machine learning research easy.

[Torch][] – provides a Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu)

[Deep Learning Tutorials][] – examples of how to *do* Deep Learning with Theano (from LISA lab at University of Montreal)

[DeepLearnToolbox][] – A Matlab toolbox for Deep Learning (from Rasmus Berg Palm)

[Cuda-Convnet][] – A fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm.

[Deep Belief Networks][]. Matlab code for learning Deep Belief Networks (from Ruslan Salakhutdinov).

[matrbm][]. Simplified version of Ruslan Salakhutdinov’s code, by Andrej Karpathy (Matlab).

[deepmat][]\- Deepmat, Matlab based deep learning algorithms.

[Estimating Partition Functions of RBM’s][Estimating Partition Functions of RBM_s]. Matlab code for estimating partition functions of Restricted Boltzmann Machines using Annealed Importance Sampling (from Ruslan Salakhutdinov).

[Learning Deep Boltzmann Machines ][Learning Deep Boltzmann Machines]Matlab code for training and fine-tuning Deep Boltzmann Machines (from Ruslan Salakhutdinov).

The [LUSH][] programming language and development environment, which is used @ NYU for deep convolutional networks

[Eblearn.lsh][] is a LUSH-based machine learning library for doing Energy-Based Learning. It includes code for “Predictive Sparse Decomposition” and other sparse auto-encoder methods for unsupervised learning. [Koray Kavukcuoglu][] provides Eblearn code for several deep learning papers on this [page][].

[Eblearn][] is a C++ machine learning library with a BSD license for energy-based learning, convolutional networks, vision/recognition applications, etc. EBLearn is primarily maintained by [Pierre Sermanet][] at NYU.

[cudamat][] is a GPU-based matrix library for Python. Example code for training Neural Networks and Restricted Boltzmann Machines is included.

[Gnumpy][] is a Python module that interfaces in a way almost identical to numpy, but does its computations on your computer’s GPU. It runs on top of cudamat.

The [CUV Library][] (github [link][]) is a C++ framework with python bindings for easy use of Nvidia CUDA functions on matrices. It contains an RBM implementation, as well as annealed importance sampling code and code to calculate the partition function exactly (from [AIS lab][] at University of Bonn).

[3-way factored RBM][] and [mcRBM][] is python code calling CUDAMat to train models of natural images (from [Marc’Aurelio Ranzato][Marc_Aurelio Ranzato]).

Matlab code for training [conditional RBMs/DBNs][conditional RBMs_DBNs] and [factored conditional RBMs][] (from [Graham Taylor][]).

[mPoT][] is python code using CUDAMat and gnumpy to train models of natural images (from [Marc’Aurelio Ranzato][Marc_Aurelio Ranzato]).

If your software belongs here, [email us][] and let us know.

[Theano]: http://deeplearning.net/software/theano
[Pylearn2]: https://github.com/lisa-lab/pylearn2
[Torch]: http://www.torch.ch/
[Deep Learning Tutorials]: http://deeplearning.net/tutorial
[DeepLearnToolbox]: https://github.com/rasmusbergpalm/DeepLearnToolbox
[Cuda-Convnet]: http://code.google.com/p/cuda-convnet/
[Deep Belief Networks]: http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html
[matrbm]: http://code.google.com/p/matrbm/
[deepmat]: https://github.com/kyunghyuncho/deepmat
[Estimating Partition Functions of RBM_s]: http://www.cs.toronto.edu/~rsalakhu/rbm_ais.html
[Learning Deep Boltzmann Machines]: http://web.mit.edu/~rsalakhu/www/DBM.html
[LUSH]: http://lush.sourceforge.net/
[Eblearn.lsh]: http://cs.nyu.edu/~koray/wp/?page_id=29
[Koray Kavukcuoglu]: http://cs.nyu.edu/~koray/wp/
[page]: http://cs.nyu.edu/~koray/wp/?page_id=17
[Eblearn]: http://eblearn.sourceforge.net/index.shtml
[Pierre Sermanet]: http://cs.nyu.edu/~sermanet/
[cudamat]: http://code.google.com/p/cudamat/
[Gnumpy]: http://www.cs.toronto.edu/~tijmen/gnumpy.html
[CUV Library]: http://www.ais.uni-bonn.de/deep_learning/downloads.html
[link]: https://github.com/deeplearningais/CUV
[AIS lab]: http://www.ais.uni-bonn.de/
[3-way factored RBM]: http://www.cs.toronto.edu/~ranzato/publications/factored3wayRBM/code/factored3wayBM_04May2010.zip
[mcRBM]: http://www.cs.toronto.edu/~ranzato/publications/mcRBM/code/mcRBM_04May2010.zip
[Marc_Aurelio Ranzato]: http://www.cs.toronto.edu/~ranzato
[conditional RBMs_DBNs]: http://www.cs.nyu.edu/~gwtaylor/publications/nips2006mhmublv/code.html
[factored conditional RBMs]: http://www.cs.nyu.edu/~gwtaylor/publications/icml2009/code/
[Graham Taylor]: http://www.cs.nyu.edu/~gwtaylor/
[mPoT]: http://www.cs.toronto.edu/~ranzato/publications/mPoT/mPoT.html
[email us]: mailto:admin@deeplearning.net